Performance Prediction of Long-Term Anaerobic Digestion Operation of Food Waste Using a Combined Approach of Time-Series Analysis Techniques and Biomethane Potential Test Results

Predicting long-term anaerobic digestion (AD) performance for food waste remains challenging because of substrate variability, process disturbance, and limited routine monitoring data. This study developed a practical framework that combines biomethane potential (BMP) test results with time-series analyses to estimate methane production during steady-state long-term AD operation. Ten paired batch and long-term datasets from three research groups were analysed. Among four BMP kinetic models, the Cone model gave the best fit in eight of 10 datasets. For long-term prediction, a 3-day sliding-window method and two Kalman filter approaches were compared. The one-dimensional Kalman filter achieved the best overall predictive accuracy, while the two-dimensional Kalman filter, which incorporated substrate conversion efficiency, provided clearer identification of persistent abnormal deviations associated with potential inhibition. The proposed framework offers a simple and localised decision support tool for methane forecasting, noise reduction, and early warning of instability when only BMP data and routine methane measurements are available.